---
id: promptflow-devkit
version: "1.18.5"
license: MIT
license_treatment: permissive
maintenance: active
---

# promptflow-devkit — Prompt flow devkit

License: permissive · Maintenance: active · Downloads: 211.7K/mo

## What it is and what it does

Promptflow-devkit is a development toolkit for building and iterating on LLM application flows. It sits between the minimal promptflow-core package (for execution only) and the full Azure-integrated promptflow-azure package, offering a middle ground for local and on-premises development. The package provides debugging and tracing capabilities to help developers understand LLM interactions, evaluate flow quality against datasets, and prepare flows for production deployment.

The toolkit includes a tracing collector and UI for observability, integration points for CI/CD testing, and support for deploying flows to various serving platforms or embedding them in application code. It depends on 22 runtime packages including Flask for UI components, SQLAlchemy for data handling, Azure Monitor for telemetry export, and utilities like GitPython and python-dotenv for common development workflows.

Use it for:

- Debug LLM interactions and flow logic iteratively during development with built-in tracing and UI.
- Evaluate flow quality and performance against larger datasets before production deployment.
- Integrate flow testing into CI/CD pipelines to maintain quality across releases.
- Deploy flows to custom serving platforms or embed them directly into application code.
- Monitor and observe LLM application behavior through integrated telemetry collection.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides a development toolkit for building, debugging, evaluating, and deploying LLM application flows with integrated tracing and observability.

Yes, if you are developing LLM applications locally or on-premises and need debugging, evaluation, and deployment tooling. The active maintenance, permissive MIT license, and low install friction make it a solid choice for iterative flow development. If you only need to execute pre-built flows in production, consider the lighter promptflow-core package instead; if you require Azure cloud integration, use promptflow-azure.

## Install

pip install promptflow-devkit
uv add promptflow-devkit
poetry add promptflow-devkit

## Installing promptflow-devkit

Before you install: Low install friction with a pure-Python wheel distribution. Active maintenance with recent commits and a large community (11217 stars). Supports current Python versions (3.9–3.14).

License in practice: MIT license permits commercial and private use with minimal restrictions, making it suitable for both open-source and proprietary projects.

Quickstart:

pip install promptflow-devkit

from promptflow_devkit import flow

# Load and debug a flow
my_flow = flow.load(source='path/to/flow')

Requires Python 3.9 or later; 22 runtime dependencies including Azure monitoring, Flask, and SQLAlchemy may increase installation size.

Verify before relying:

- Whether the tracing and observability features work without Azure Monitor setup or if that is a required dependency.
- Specific performance characteristics when evaluating flows against large datasets.
- Whether CI/CD integration requires additional configuration beyond what the package provides.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 211.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

llm flow development framework, prompt flow devkit, debug and iterate llm applications, flow evaluation and testing, llm application observability, prompt flow tracing, flow deployment toolkit, llm-development, flow-orchestration, observability

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